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Mundy, M.

Publications and source records attributed to Mundy, M..

4 recordsLinked to original sources

Temporal Lobe Activation Predicts Episodic Memory Following Traumatic Brain Injury

The temporal lobes are critical for episodic memories and are preferentially affected following a traumatic brain injury (TBI). As such, episodic memory difficulties are common following TBI; however, the underlying neural changes that precipitate or maintain these difficulties in the early phase of recovery remains poorly understood. Here, we use functional magnetic resonance imaging (fMRI) to interrogate the relationship of temporal lobe activation in response to face, scene, and animal stimuli. Twenty-five patients with moderate to severe TBI were recruited an average of 2 months post-injury and compared with 21 demographically similar healthy controls. Findings indicate that memory for faces was preferentially impaired, compared to scene and animal stimuli. Decreased activity in temporal lobe structures was present for both face (right transverse temporal gyrus) and scene stimuli (right fusiform gyrus), but not for animals. Greater activation in these structures was associated with better long-term recognition. These findings provide evidence to suggest that TBI: a) preferentially affects memory for complex stimuli such as faces and scenes, and b) causes aberrant neuronal processes despite lack of evidence of significant impairment in behavioural performance. The mechanisms underpinning these findings are discussed in terms of differences in strategy use and reduced neural efficiency.

neuroscience↗

The ModelSEED Database for the integration of metabolic annotations and the reconstruction, comparison, and analysis of metabolic models for plants, fungi, and microbes

For over ten years, ModelSEED has been a primary resource for the construction of draft genome-scale metabolic models based on annotated microbial or plant genomes. Now being released, the biochemistry database serves as the foundation of biochemical data underlying ModelSEED and KBase. The biochemistry database embodies several properties that, taken together, distinguish it from other published biochemistry resources by: (i) including compartmentalization, transport reactions, charged molecules and proton balancing on reactions;; (ii) being extensible by the user community, with all data stored in GitHub; and (iii) design as a biochemical "Rosetta Stone" to facilitate comparison and integration of annotations from many different tools and databases. The database was constructed by combining chemical data from many resources, applying standard transformations, identifying redundancies, and computing thermodynamic properties. The ModelSEED biochemistry is continually tested using flux balance analysis to ensure the biochemical network is modeling-ready and capable of simulating diverse phenotypes. Ontologies can be designed to aid in comparing and reconciling metabolic reconstructions that differ in how they represent various metabolic pathways. ModelSEED now includes 33,978 compounds and 36,645 reactions, available as a set of extensible files on GitHub, and available to search at https://modelseed.org and KBase.

systems biology↗

Distinct Microbes, Metabolites, and Ecologies Define the Microbiome in Deficient and Proficient Mismatch Repair Colorectal Cancers

Background\n\nThe link between colorectal cancer (CRC) and the gut microbiome has been established, but the specific microbial species and their role in carcinogenesis remain controversial. Our understanding would be enhanced by better accounting for tumor subtype, microbial community interactions, metabolism, and ecology.\n\nMethods\n\nWe collected paired colon tumor and normal-adjacent tissue and mucosa samples from 83 individuals who underwent partial or total colectomies for CRC. Mismatch repair (MMR) status was determined in each tumor sample and classified as either deficient MMR (dMMR) or proficient MMR (pMMR) tumor subtypes. Samples underwent 16S rRNA gene sequencing and a subset of samples from 50 individuals were submitted for targeted metabolomic analysis to quantify amino acids and short-chain fatty acids. A PERMANOVA was used to identify the biological variables that explained variance within the microbial communities. dMMR and pMMR microbial communities were then analyzed separately using a generalized linear mixed effects model that accounted for MMR status, sample location, intra-subject sample correlation, and read depth. Genome-scale metabolic models were then used to generate microbial interaction networks for dMMR and pMMR microbial communities. We assessed global network properties as well as the metabolic influence of each microbe within the dMMR and pMMR networks.\n\nResults\n\nWe demonstrate distinct roles for microbes in dMMR and pMMR CRC. Sulfidogenic Fusobacterium nucleatum and hydrogen sulfide production were significantly enriched in dMMR CRC, but not pMMR CRC. We also surveyed the butyrate-producing microbial species, but did not find a significant difference in predicted or actual butyrate production between dMMR and pMMR microbial communities. Finally, we observed that dMMR microbial communities were predicted to be less stable than pMMR microbial communities. Community stability may play an important role in CRC development, progression, or immune activation within the respective MMR subtypes.\n\nConclusions\n\nIntegrating tumor biology and microbial ecology highlighted distinct microbial, metabolic, and ecological properties unique to dMMR and pMMR CRC. This approach could critically improve our ability to define, predict, prevent, and treat colorectal cancers.

cancer biology↗

ProbAnnoWeb and ProbAnnoPy: probabilistic annotation and gap-filling of metabolic reconstructions

SummaryGap-filling is a necessary step to produce quality genome-scale metabolic reconstructions capable of flux-balance simulation. Most available gap-filling tools use an organism-agnostic approach, where reactions are selected from a database to fill gaps without consideration of the target organism. Conversely, our likelihood based gap-filling with probabilistic annotations selects candidate reactions based on a likelihood score derived specifically from the target organisms genome. Here, we present two new implementations of probabilistic annotation and likelihood based gap-filling: a web service called ProbAnnoWeb, and a standalone python package called ProbAnnoPy.\n\nAvailability and ImplementationOur tools are available as a web service with no installation needed (ProbAnnoWeb), available at http://probannoweb.systemsbiology.net, and as a local python package implementation (ProbAnnoPy), available for download at http://github.com/PriceLab/probannopy.\n\nContacthttp://Evangelos.Simeonidis@systemsbiology.org; http://Nathan.Price@systemsbiology.org

systems biology↗